Probabilistic Physics Digital Twin for Human-Robot Interaction
cs.RO
Submitted: 2026-10-14
Updated: 2026-10-17
Code: https://github.com/luca-medeiros/lang-segment-anything
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- DensePhysNet: Learning Dense Physical Object Representations via Multi-step Dynamic Interactions
- Church: a language for generative models
- From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought
- A High-Fidelity Digital Twin for Robotic Manipulation Based on 3D Gaussian Splatting
- Splat-MOVER: Multi-Stage, Open-Vocabulary Robotic Manipulation via Editable Gaussian Splatting
- PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identification
- Physics3D: Learning Physical Properties of 3D Gaussians via Video Diffusion
- Qwen3 Technical Report
- Intel RealSense Stereoscopic Depth Cameras
- Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
- SAM 2: Segment Anything in Images and Videos
- FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects
- cuRobo: Parallelized Collision-Free Minimum-Jerk Robot Motion Generation
Related papers
- FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
- MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
- RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
- ARC: A Reasoning Recipe for Robot Foundation Models
- HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies